sberrio/Mixed-Signals-V2X

Mixed Signals V2X: Collaborative 3D Object Detection Dataset

Point clouds and 3D bounding-box labels for the Mixed Signals dataset, a diverse, real-world dataset for heterogeneous LiDAR V2X collaboration (ICCV 2025). Collected at a busy intersection with 3 connected vehicles and a roadside unit (RSU) carrying two LiDARs, for 5 LiDAR sensors per synchronized frame.

This repository accompanies a collaborative 3D object detection competition on Codabench, built on the Mixed Signals dataset.

Competition

A predictions-only collaborative 3D detection challenge (Vehicle / Bike / Pedestrian) scored by BEV rotated-box mAP. Two phases: Development (public validation split, Aug 1 to 28 2026), then Final (held-out test split, Aug 29 to Sep 4 2026). Download the data here, train locally, and submit per-frame prediction files on Codabench. See the competition's starting kit for the submission format.

Contents

The dataset is delivered as per-segment .tar archives (not loose files), so downloads stay fast and resumable. One archive is one ~30 s scene.

train/  mini_<N>.tar     # 29 archives: point clouds + odometry + labels
val/    mini_<N>.tar     #  4 archives: point clouds + odometry
test/   test-XXXX.tar    # ~6 pooled archives: anonymized bundles (see Splits)

Each train or val archive extracts to the native layout:

PointClouds/            # per-agent point clouds (ASCII .pcd: x y z intensity)
  mini_<N>/
    top_<k>_<ts>.pcd    dome_<k>_<ts>.pcd     # RSU lidars
    003_<k>_<ts>.pcd    004_<k>_<ts>.pcd    laser_<k>_<ts>.pcd   # vehicles
Odometry/               # per-vehicle map-frame poses (nav_msgs/Odometry CSVs)
  mini_<N>/ odometry_{003,004,laser}.csv
labels/                 # 3D box ground truth: TRAINING ARCHIVES ONLY
  mini_<N>/ mini_<N>_<k>.txt

Each test archive extracts to self-contained anonymized bundles, one folder per frame, containing the 5 agent clouds plus a precomputed transforms.json:

<token>/ 003.pcd 004.pcd dome.pcd laser.pcd top.pcd  transforms.json

Downloading and using

Download only the segments you need and extract in place. The archives rebuild the folder layout the dataloader reads:

# one train segment
hf download sberrio/Mixed-Signals-V2X train/mini_6.tar --repo-type dataset --local-dir .
tar xf train/mini_6.tar          # -> PointClouds/mini_6 + Odometry/mini_6 + labels/mini_6

# the whole test split (a handful of archives)
hf download sberrio/Mixed-Signals-V2X --repo-type dataset --include "test/*" --local-dir .
for f in test/test-*.tar; do tar xf "$f" -C test/; done   # -> test/<token>/...

Then load frames with the starting kit's msig_dataloader.py (native backend for train/val, bundle backend for test). No loader changes are needed.

Splits

37 segments (mini_1 … mini_37), each a ~30 s synchronized sequence.

Split	Segments	Labels here?
Train	29 segments	✅ yes
Validation	4 segments	❌ withheld (Development leaderboard)
Test	4 segments	❌ withheld (Final leaderboard)

Point clouds are provided for all 37 segments. Only train labels are public. Validation and test labels are held out for the competition leaderboards.

Label format

labels/mini_<N>/mini_<N>_<syncIndex>.txt, one file per synchronized frame. The frame id mini_<N>_<syncIndex> pairs with the point clouds: sync step k of segment mini_N is the clouds top_k…, dome_k…, 003_k…, 004_k…, laser_k….

Each line is one 3D box, with 8 whitespace-separated columns:

cls  cx  cy  cz  dx  dy  dz  yaw

Field	Meaning	Units
cls	class: 1=Vehicle, 2=Bike, 3=Pedestrian	n/a
cx cy cz	box centre, "top" (RSU) ego frame	metres
dx dy dz	length, width, height	metres
yaw	heading (rotation about +z)	radians

Empty files are frames with no labelled objects. Boxes are restricted to x, y ∈ [-51.2, 51.2] m and contain >5 aggregated lidar points. The 10 fine-grained annotation classes are grouped into the 3 meta-classes above.

Training counts: 29 segments, 8,394 frames, 124,668 boxes (Vehicle 48,721, Bike 30,893, Pedestrian 45,054).

Coordinate frame

All boxes are in the "top" RSU LiDAR (ego) frame. The two RSU lidars are static. The three vehicles' poses are in Odometry/. Predictions for the competition must also be in the "top" frame.

License and citation

Released under CC BY-NC-SA 4.0 (attribution, non-commercial, share-alike). If you use this data, please cite:

@inproceedings{luo2025mixed,
  title     = {Mixed Signals: A Diverse Point Cloud Dataset for
               Heterogeneous LiDAR V2X Collaboration},
  author    = {Luo, Katie Z. and Dao, Minh-Quan and Liu, Zhenzhen and
               Campbell, Mark and Chao, Wei-Lun and Weinberger, Kilian Q. and
               Malis, Ezio and Fr\'emont, Vincent and Hariharan, Bharath and
               Shan, Mao and Worrall, Stewart and Berrio Perez, Julie Stephany},
  booktitle = {Proceedings of the IEEE/CVF International Conference on
               Computer Vision (ICCV)},
  year      = {2025}
}